Data Science Projects for Freelancers

6+ years of professional experience in AI/ML, Machine Learning Engineering, Data Science, Software Engineering, or a related field.
Strong proficiency in Python and experience writing production-quality code.
Solid understanding of machine learning concepts, including supervised and unsupervised learning.
Hands-on experience with ML frameworks and libraries such as Scikit-learn, TensorFlow, PyTorch, or Keras.
Strong experience working with data using tools such as Pandas and NumPy.
Experience with exploratory data analysis, feature engineering, model evaluation, hyperparameter tuning, and performance optimization.
Understanding of APIs, software development workflows, and integrating ML models into applications.
Experience with Git/version control and basic software engineering best practices.
Strong problem-solving, analytical, and data interpretation skills.
Ability to work independently, manage project deliverables, and meet deadlines within a short project timeline.
Strong communication skills and the ability to collaborate effectively with technical and non-technical stakeholders.

Seeking a GSI data analyst to support reporting compliance for an advertising contract with a National Guard division. The role includes reviewing and analyzing GSI data, identifying discrepancies, and helping ensure accurate reporting for contract obligations. You will collaborate with internal teams to maintain compliance, document findings, and provide clear recommendations. Experience working with advertising or contract reporting is preferred, along with strong attention to detail and the ability to communicate issues clearly. Will be required to provide monthly analysis reports by the 3rd of each month. Remote work available.

Hello, John. I am looking for a long-term technical mentor for my son David, who is completing a master’s degree in Biology with a bioinformatics focus.

His graduate project is called the Organelle Quality Hub. It is a scientific web application built with Python, Django, and PostgreSQL.

The platform will store existing information about mitochondrial and chloroplast genomes. Users will be able to search, filter, compare, and visualize records. It will also include a metadata-completeness indicator showing which expected fields are present or missing.

David already has part of the website and database working. He needs guidance reviewing the existing architecture, organizing the remaining work, resolving technical problems, importing and validating data, testing the application, and completing weekly deliverables.

We are not looking for someone to build the application for him. David wants to write, test, and understand all the code himself. The mentor should guide him through screen-sharing sessions while David controls and modifies the code.

We are looking for support throughout the semester, with the possibility of continuing for the full academic year.

Our initial plan is:

- Two virtual sessions per week
- 60 minutes per session
- Eight sessions per month
- Long-term technical guidance and code review

I would like to confirm:

1. Do you have strong experience with Django and PostgreSQL?
2. Have you mentored students or junior developers through a complete software project?
3. Can you review an existing application and create prioritized weekly deliverables?
4. Are you comfortable teaching through screen sharing while David controls the code?
5. Have you worked with scientific data, genomics data, NCBI, GenBank, or other structured research databases?
6. Are you available for two sessions per week throughout the semester?
7. What monthly package rate would you offer for eight 60-minute sessions?
8. Would you offer a reduced rate for a semester-long arrangement?
9. Are you available for one paid trial session this week?

This is a mentoring role, not a development outsourcing role.

Thank you.

NOTE: This is purely intellectual partnership. Money is not a motivation in this project.

Seeking an established researcher for long-term collaboration in computational/structural biology.

Requirements:

* PhD in CS, computational science, bioinformatics, statistics, or related field
* Established publication record
* Professional standing to serve as a future academic/industry referee

Collaboration:

* Strengthen methodology and experimental design
* Perform supplementary analyses
* Co-write manuscripts and reviewer responses
* Provide a reference letter after an established collaboration

Full co-authorship for contributions meeting authorship standards, with opportunities for follow-up publications.

Skills: Statistics, Machine Learning (ML), Physics, Statistical Analysis, Data Science, Computer Science, Data Analysis, Academic Research, Computational Analysis, Bioinformatics

About Us
We are a global IT consulting firm headquartered in Atlanta, GA, dedicated to building successful IT careers. We support our clients with project-based staffing across various technologies, including Android and iPhone Mobile Development, AWS, Big Data, Data Science, Exchange, Cybersecurity, and Azure. With a growing base of around 500 clients globally, we've successfully built careers for thousands over the past two decades. Our mission is to help individuals and businesses find, build, and grow their next big dream.
About the Role
We are looking for a driven and detail-oriented IT Recruiter to join our growing team. In this role, you will be responsible for managing end-to-end recruitment processes, building relationships with candidates and hiring managers, and ensuring we attract top-tier IT talent across North America. You'll play a key role in shaping our talent acquisition efforts by combining smart sourcing strategies with strong candidate engagement and process management.
We are looking for a motivated and results-driven Recruiter to join our growing team. This is a fully remote opportunity for someone who thrives in a fast-paced environment and is comfortable handling high-volume outbound calls daily.
If you enjoy speaking with candidates, screening talent quickly, and working with targets — this role is for you.
Key Responsibilities
• Conduct high-volume outbound calls (100–150+ calls per day) to source and screen candidates
• Qualify candidates based on client requirements
• Schedule interviews and coordinate with hiring managers
• Maintain accurate candidate records in ATS/CRM systems
• Follow up consistently with candidates
• Meet daily and weekly recruitment KPIs
• Source candidates through job portals, LinkedIn, referrals, and other platforms
Requirements
• 1–3+ years of experience in recruitment, sales, telemarketing, or high-volume calling roles
• Excellent verbal communication skills in English (B2+ or Native level preferred)
• Comfortable making 100+ calls per day
• Strong organizational and time-management skills
• Ability to work independently in a remote environment
• Stable internet connection and quiet workspace
Compensation
• Independent contractor (1099) structure
• Competitive monthly compensation (USD-based)
• Performance-based incentives
Ideal Candidate Profile
• Energetic and confident on calls
• Target-oriented and self-motivated
• Thick-skinned and resilient (not discouraged by rejection)
• Comfortable working in a fast-paced, metrics-driven environment

I am building a secure knowledge system that gets deployed inside a client's own environment, and applications that run on top of it. I need someone who has done this before: not a chatbot demo, but a real system holding confidential material that people rely on daily and that has passed an enterprise security review.

Security

The data going into these systems is confidential customer research, unpublished strategy, client work under NDA, and in some cases commercially sensitive material that would cause serious damage if it leaked. The system therefore lives with the client, inside their own infrastructure and their own security perimeter, running against their model provider. Nothing sits in a shared environment I control, and nothing routes through a third party the client has not already approved.

That decision shapes the architecture from the first line of code rather than being a setting we switch on later. Encryption at rest and in transit, access control that mirrors the permissions structure the client already has, audit logging detailed enough to answer "who saw what, and when" a year after the fact, and a deployment model that can survive an infosec questionnaire and a penetration test.

What I want built

A secure, client-hosted knowledge system. I'd like to build two versions:

Version one is for agencies. It will ingest everything they have produced: past pitches, case studies, sector points of view, research decks, articles nobody has opened since 2019, and make it genuinely searchable and usable. Two ways in: a chatbot their team can ask questions of, and a live connection that other AI tools plug into and work from. The second matters more than the first. When someone is writing a proposal, they should be able to pull the three most relevant case studies without already knowing those case studies exist. When someone is drafting an article, they should be building on the firm's own published position rather than starting from nothing.

The hard part is not the chat interface. It is that half the corpus is PowerPoint, the argument lives in slide layouts and chart images, and the metadata is whatever someone typed into a filename in 2017.

Version two is for insight teams inside large organisations. Same spine, higher stakes. Years of research reports, survey data, focus group transcripts, tracker studies and brand health work, ingested and organised so an insight manager can answer a question in ninety seconds instead of emailing three colleagues and waiting a day.

The applications that sit on top

The data centre is the foundation, not the product. Once it exists, everything interesting happens in the layer above it, and this is where I expect most of the build effort to go over time.

A voice interface, because asking a question out loud and getting a straight answer back is a different behaviour from searching, and it puts the material into meetings and workshops where nobody is going to stop and open a search box.

Persona generation grounded in the client's actual research rather than invented, so that a persona is traceable back to the studies and quotes it came from and can be interrogated rather than just read.

Predictive modelling and simulation against the insight base: forecasting how a segment behaves, testing a proposition against what the research already says about the people it targets, running scenarios before anyone commissions new fieldwork.

Proactive research that runs on its own initiative against a defined customer profile, finds what has changed in that customer's world, and files it back into the vault with full provenance.

I am being deliberate about the order. Retrieval has to be excellent before any of this is worth building, because every application above inherits its accuracy from the layer underneath.

What the job actually involves

Early on, the unglamorous half: an ingestion pipeline that handles PowerPoint, PDF, Word, spreadsheets and transcripts without quietly losing the content that matters, and a chunking and metadata approach that makes the difference between search that works and search that returns plausible nonsense.

Then the retrieval layer, and a way of measuring whether it is any good. I want to know when a pipeline change has made results worse, and I want to know it from an eval set rather than from a client telling me.

Then packaging the whole thing so it deploys repeatably into very different client environments, AWS in one, Azure in another, each with their own identity provider, their own approved models and their own rules. This is the engineering problem that decides whether the product scales past the first two clients, and it is easy to underestimate.

Then the interfaces and the applications above: chat, a connection layer so other AI tools can query the vault as a live source, voice, personas, simulation.

Experience that you'll need

Shipped a retrieval system into production that people used regularly, and can talk in detail about how you improved its accuracy over time
Built and deployed software into a client's own cloud environment rather than only running your own
Handled real security requirements: SSO and SAML, role-based access down to document level, encryption, audit logging, and the paperwork that comes with selling into large organisations
Built document ingestion for genuinely awkward formats, especially slide decks

Worked seriously with vector search, whether that is pgvector, Qdrant, Weaviate or something else, and can explain why you chose it

Strong backend engineering, most likely Python or TypeScript, with enough infrastructure and deployment knowledge to run this without me hiring a second person

Useful, not essential

Voice and realtime speech pipelines. Agent orchestration and tool use. Writing MCP servers. A statistics or data science background, which matters for the simulation and persona work more than for the retrieval work. Experience taking a product through a corporate infosec review.


Practicalities

Contract to begin with, [x days a week], reviewed after the first build. Remote is fine but needs to be at least 3 hrs with GMT, because I want to be able to talk things through the same day rather than the next one. Rate depending on experience. There is a real chance this becomes something longer and larger if the first builds land, and I would rather find one person to grow with than run a series of short engagements.

How to apply

Apply with a short note covering two things.

One: the hardest retrieval or search quality problem you have solved, what you tried that did not work, and how you knew when it was fixed.

Two: the thing in this spec you think I have got wrong.

Thanks

Executive in consumer-direct financial services seeking an experienced advisor/coach to provide high-level consultative guidance on lead strategy, contact routing, and data science architecture. This is an advisory/coaching engagement, not an implementation role. We are migrating to Databricks and need a thought partner to pressure-test our roadmap, discuss best practices, and advise on optimal workflows. Key topics include lead routing, contact orchestration, analytics alignment, and operating model structure. Initial discovery session plus potential ongoing advisory calls as we design and execute our strategy.

What You’ll Do
• Design and build NLP pipelines to parse and extract data from complex construction documents
• Develop and deploy LLM-powered systems using RAG and fine-tuning on domain-specific data
• Build and maintain ETL pipelines to ingest structured and unstructured project data
• Engineer conversational AI interfaces for role-based data querying
• Design hallucination mitigation and output validation layers for high-stakes deliverables
• Establish MLOps infrastructure — model versioning, evaluation, retraining, and cloud deployment
• Collaborate directly with the founder to validate AI outputs against deep domain expertise

Required Qualifications
• 3+ years of hands-on AI/ML engineering experience in production environments
• Expert Python skills — NumPy, Pandas, scikit-learn, PyTorch or TensorFlow
• Deep NLP experience — document parsing, named entity recognition, text classification, summarization
• Proven LLM integration experience — OpenAI, Anthropic, Mistral, or similar; prompt engineering, chain-of-thought, RAG pipelines
• Vector database experience — Pinecone, Weaviate, ChromaDB, or equivalent
• Model deployment skills — FastAPI, Docker, Kubernetes, REST APIs
• Cloud platform experience — AWS SageMaker, Azure ML, or GCP Vertex AI
• Strong SQL and data engineering skills — ETL pipeline design, structured data ingestion
• Experience with model evaluation frameworks — BLEU, ROUGE, precision/recall, human-in-the-loop validation

Elevate Your Limits. A modern, conversion-oriented, multi-page fitness gym website built with semantic HTML5, Vanilla CSS (design tokens & custom properties), and Vanilla JavaScript (ES6+). Inspired by leading fitness brands with a dark luxury aesthetic.

Skills: Java, Python, Website Design, CSS, Cloud Computing, Software Architecture, Machine Learning (ML), Data Science, Web Development, Full Stack Development

We need a freelance financial analyst to update our market data reporting on a quarterly basis. You must have an active license for a Bloomberg terminal or FactSet. We require the exact data fields from these platforms to maintain our historical tracking.

The project involves replicating a specific quarterly data pull across 13 index tabs. You will use our provided template named Data Pull IPE Article 2025.01.11.xlsx. The required indices include the S&P 500, Barclays US Aggregate, MSCI EAFE, and S&P GSCI Commodity Index. You will pull the quarterly gross dividend total returns for each asset class, and you will append the new quarter to the existing data series.

We operate multi-store e-commerce brands on Shopify in two main verticals:

Home & Furniture: A large catalog (250,000+ SKUs dropshipping + local manufacturing) and a separate premium furniture storefront.

K-Beauty & Cosmetics: A dedicated store curating trending Korean skincare products and active formulations.

We already have PPC and technical teams running campaigns. What we are missing is commercial merchandising intelligence. We need a seasoned e-commerce strategist to build a practical, data-driven system to spot product trends, track competitor prices, and pick high-margin "Hero Products" before we spend a single euro on advertising.

Key Deliverables & Responsibilities
1. Trend Hunting & Product Discovery System:

Build a weekly workflow to spot upcoming product trends in Furniture & Home Decor (EU/Global trends) and Korean Skincare / K-Beauty (viral ingredients, trending brands, social commerce spikes).

Define specific data sources (Google Merchant Center Insights, Pinterest Trends, Amazon Movers & Shakers, TikTok Creative Center, Olive Young benchmarks).

2. Competitor Price Intelligence & Monitoring (MVP):

Set up an automated or semi-automated price-tracking system (using Google Merchant Center Price Competitiveness, scrapers, or tools like Price2Spy/Prisync) to benchmark our prices against competitors daily.

3. SKU Selection & Unit Economics Matrix:

Create a mathematical filtering framework (in Google Sheets/Excel) to review supplier catalogs (1,000–50,000 SKUs) and select the top 30–50 Hero SKUs based on demand, gross margin (above 35%), retail price (above €80 for furniture), and shipping feasibility.

Identify high-elasticity products to help us negotiate volume discounts with suppliers.

4. Pricing & Shipping Strategy:

Optimize our pricing and shipping models (e.g., product price + shipping vs. higher price with free shipping) for Google Shopping rank vs. on-site conversion rate.

5. 12-Month Commercial Seasonal Calendar:

Map out a 12-month promotional calendar covering seasonal peaks, supplier lead times, and promotional bundle concepts for both niches.

Who We Are Looking For
Proven experience as an E-Commerce Merchandiser, Category Manager, or Commercial E-Commerce Strategist.

Practical experience in Home/Furniture OR K-Beauty/Cosmetics (experience in both is a major plus).

Deep understanding of Shopify, Google Merchant Center (Market Insights, Price Competitiveness, Best Sellers), and feed segmentation.

Hands-on ability to build clear, usable spreadsheets and dashboards (no complex theoretical slides—we need functional tools).

Screening Questions (Please answer when applying)
Have you built a product trend discovery or competitor price tracking system for an e-commerce brand before? Please share a brief 2–3 sentence example.

What tools or workflows would you recommend to track K-Beauty and Furniture trends without spending thousands on enterprise software?

What is the first thing you would build in the first 48 hours as an MVP for this project?

I’m looking for an experienced Senior/Staff Software Engineer to help me work through advanced software-engineering benchmark and AI-evaluation projects, primarily involving Mercor Hard Colosseum–style tasks and Snorkel software-engineering benchmark projects.

These projects involve realistic software-engineering problems such as understanding existing repositories, debugging complex issues, implementing fixes, reviewing code, working with tests, understanding grading requirements, and identifying edge cases.

I am looking for someone who can work with me as a technical coach, pair programmer, debugger, and code reviewer.

You should be comfortable jumping into unfamiliar repositories and quickly understanding:

Existing architecture
Task requirements
Failing tests
Hidden edge cases
Repository structure
Build/test environments
Docker or containerized environments
CI/CD behavior
Git/GitHub workflows
Code correctness beyond simply passing tests
What I Need Help With

You may help me with tasks such as:

Understanding difficult benchmark/task requirements
Reviewing task specifications before implementation
Exploring unfamiliar GitHub repositories
Identifying the relevant parts of a large codebase
Debugging failing builds and tests
Explaining why tests are failing
Finding edge cases that may not be immediately obvious
Reviewing my implementation before submission
Suggesting cleaner or more robust solutions
Identifying regressions introduced by a change
Running and interpreting test suites
Understanding Docker-based development environments
Troubleshooting environment/setup issues
Reviewing patches and Git diffs
Evaluating whether a solution actually satisfies the requirements
Helping distinguish between a solution that merely passes visible tests and one that is genuinely correct
Improving development speed when working through unfamiliar projects

The goal is not simply to generate code, but to reason carefully about each engineering problem and make sure the resulting implementation is technically sound.

Typical Workflow

For each task, we would generally:

Review the task requirements together.
Inspect the repository and existing architecture.
Identify the files/components involved.
Discuss the likely implementation approach.
Work through difficult implementation or debugging issues.
Run the available tests.
Investigate failures and edge cases.
Review the final diff.
Check for regressions, maintainability issues, and hidden failure scenarios.
Make sure I fully understand the solution before completing the task.
Required Experience

I’m looking for someone with strong real-world software-engineering experience.

You should have significant experience with several of the following:

Python
TypeScript / JavaScript
Go
Rust
C# / .NET
React / Node.js
Backend development
REST APIs
SQL / databases
Linux
Git
GitHub
Docker
CI/CD
Automated testing
Unit and integration testing
Debugging unfamiliar codebases

You do not need to be an expert in every language.

More important is your ability to understand unfamiliar systems quickly and reason through difficult engineering problems.

Strongly Preferred

I’m especially interested in engineers who have experience with:

Senior or Staff-level software engineering
Open-source projects
GitHub pull-request reviews
SWE-bench or similar coding-agent benchmarks
AI coding-agent evaluation
LLM evaluation
Benchmark creation or validation
Repository-level coding tasks
Debugging complex test failures
Codebase modernization
Reviewing AI-generated code
Finding subtle bugs that automated tests miss

Experience with tools such as Claude Code, Cursor, Codex, GitHub Copilot, or other AI coding agents is also useful.

What Makes a Good Fit

You are probably a good fit if you can open an unfamiliar repository and quickly answer questions like:

Where is the bug most likely located?
What behavior is the task actually asking us to change?
Which tests should cover this behavior?
What could break if we modify this component?
Is this fix addressing the root cause or only making the test pass?
What edge cases are missing?
Is the implementation consistent with the existing architecture?
Could a hidden test expose a weakness in this solution?

I value reasoning and engineering judgment much more than simply producing code quickly.

Working Arrangement

This will initially be a small engagement, but it may become ongoing work if we work well together.

I would prefer someone who can occasionally collaborate through:

Upwork chat
Screen sharing
Pair-programming sessions
Code review
Git diffs / patches
Repository walkthroughs

Please let me know your availability and preferred working style.

Important

All work must follow the applicable project/platform rules.

I am specifically looking for technical guidance, debugging assistance, pair programming, and code review. You should not access my account, impersonate me, or submit work under my identity.

When Applying

Please include:

Your years of professional software-engineering experience.
Your strongest programming languages.
Your experience debugging unfamiliar repositories.
Your experience with GitHub pull requests and code reviews.
Any experience with SWE-bench, coding benchmarks, AI evaluation, or AI coding agents.
Your experience with Docker/Linux development environments.
Your hourly rate.
Your availability.
A short example of a difficult bug or engineering problem you diagnosed.
Screening Question

Suppose you receive a task in an unfamiliar repository. Your implementation passes all existing tests, but you suspect that hidden tests may still fail.

How would you determine whether your implementation is actually correct rather than simply overfitting to the visible tests?

Please give a short but technical answer.

Engagement Type

Hourly / ongoing as needed

Potential for longer-term collaboration if the first few tasks go well.

We are an early-stage AI healthcare company looking for an exceptional "Senior Machine Learning / AI Engineer" to build a recommendation and Next-Best-Action engine using several years of longitudinal patient treatment and transaction data.

The core problem is to identify and validate patterns in historical patient behavior, then use those patterns to predict:

* What a patient is most likely to do next
* When they are likely to return
* What additional services are most relevant to recommend
* How confident we should be in each prediction

The project will have two phases:

Phase 1: Pattern Discovery & Validation
Analyze historical patient journeys to identify statistically meaningful same-visit affinities, treatment sequences, next-visit behavior, and return timing.

Phase 2: Predictive Next-Best-Action Engine
Build and backtest a patient-level recommendation engine using the validated patterns from Phase 1.

We are specifically looking for someone with strong experience in **recommendation systems, behavioral prediction, sequential modeling, applied machine learning, and transactional datasets**.

This is not primarily an LLM or generative AI project. We care about statistical rigor, explainability, strong baselines, backtesting, and production-quality ML.

Healthcare experience is helpful but not required. Prior experience building recommendation, personalization, or Next-Best-Action systems is highly preferred.

Required Application Questions

**Please answer all five questions below. Applications that do not answer them will not be considered.**

1. What is the most relevant recommendation, personalization, or Next-Best-Action system you have personally built?
Briefly describe what you predicted, your approach, and how you measured performance.

2. You have several years of customer transaction history and need to predict what a customer will purchase next and when they will return. How would you approach this problem?
Please keep your answer to 5-10 sentences.

3. How would you determine whether Treatment A actually increases the likelihood of Treatment B versus Treatment B simply being popular overall?
Briefly explain the methodology you would use.

4. How would you evaluate a Next-Best-Action model using historical data without introducing data leakage?
What metrics would you use to decide whether it is good enough to put into production?

5. Please share one relevant GitHub repository, technical case study, portfolio example, or project you personally built.
Clearly state which parts you designed and implemented yourself.

https://brainforge.ai/careers/

## **Role Overview**

Brainforge is hiring an **Omni Analyst** to serve as the lead analyst and client-facing owner on a **large CPG client** engagement. This is a hybrid delivery role: you'll do hands-on analytics and Omni work while also building trust, running client touchpoints, and driving BI adoption.

---

## **🔑 Key Responsibilities**

### **Omni analytics & implementation**

- Work in **Omni's CI/CD modeling pipeline** to build, review, and ship governed analytics content.
- Serve as the **technical brain** on Omni implementations alongside Brainforge CSOs and Omni FDEs.
- Build and maintain dashboards, reports, and self-service analytics in Omni on top of modern data platforms.

### **Client delivery & relationship ownership**

- Serve as the **primary analyst and client success owner** on the engagement—balancing technical delivery with relationship management.
- Translate business questions into clear analytics plans, deliverables, and client-ready narratives.
- Present insights and progress to client stakeholders over Slack, email, and Zoom.
- Partner with Brainforge delivery leadership to scope work, manage priorities, and keep the engagement healthy.

### **Insights & reporting**

- Perform trend and root-cause analysis to provide actionable recommendations.
- Present findings to non-technical stakeholders with clarity and confidence.
- Create reports and visualizations that communicate key metrics effectively.

---

## **What This Role Is / Is Not**

### **This role is:**

- A **client-facing analyst** who can own both the work and the relationship
- Focused on **BI tool adoption**, not just building models in isolation
- A blend of **Omni implementation expertise** and **consulting delivery**

### **This role is not:**

- A pure backend or pipeline engineering role
- A heads-down IC slot with no client communication
- A role for someone who has never been part of a **BI rollout or adoption** effort

---

## **🧩 Background & Skills**

### **Education and Experience**

- Bachelor's degree in a related field (e.g., Data Science, Statistics, Computer Science, Business).
- **3+ years** of experience as a Data Analyst, Analytics Consultant, BI Analyst, or similar role.

### **Must have**

- **Omni Quickstart certification** (required)
- **Clear communicator**—strong written and oral skills with client stakeholders
- Hands-on analytics experience with modern data platforms (**Snowflake, Redshift, and/or Databricks**)
- A track record of **BI tool adoption**—you have rolled out a BI tool or been on the team that did
- **Intermediate SQL** and comfort working with relational data in production environments
- Ability to manage multiple priorities in a fast-paced consulting environment

### **Nice to have**

- Additional **Omni certifications** beyond Quickstart
- **CPG or omnichannel retail** analytics experience
- Experience with **dbt** or similar transformation workflows
- Familiarity with other BI tools (Tableau, Looker, Sigma, Power BI, Metabase, etc.)
- Prior experience as a **Client Success Owner** or lead analyst on a client engagement

### **Hard no's**

- Profiles that are **too technical or engineer-only** with weak client communication
- Candidates with **no BI rollout or adoption experience**

---

## **🌟 Traits & Mindset**

- Excited to **work remotely with a distributed team** in US time zones
- Thrives in an async environment leveraging tools like Slack, Loom, and AI to move faster
- Passionate about digging for insights and producing **great analyses** for clients
- Curious about business models—especially how data drives decisions in CPG
- Values working with **kind**, **ambitious**, and **pragmatic** teammates.

---

## ✍️ A Day in the Life Looks Like

- Working directly with client and Brainforge stakeholders over Email/Slack/Zoom
- Building and refining dashboards and models in **Omni** on top of Snowflake (or similar)
- Partnering with CSOs, service leads, and Omni FDEs to unblock implementation work
- Preparing client updates, training sessions, and adoption materials
- Supporting the broader delivery team on analytics questions and client context

---

## **📍 Location & Logistics**

- **Location:** Remote, **US-based** (US time zones required)
- **Hours**: 20hrs/wk
- **Travel:** ~1x/month to **NYC**
- **Type:** **1099 contract** · 6 months with possible extension
- **Reports to:** Head of Delivery
- **Start Date:** ASAP

Course Topics You Should Be Comfortable Teaching

Data acquisition & preprocessing

SQL for ML: joins, CTEs, window functions, query plans (PostgreSQL/DuckDB)
Big-data querying: Parquet/ORC, Spark SQL, Trino, document stores (MongoDB)
Missing-value mechanisms (MCAR/MAR/MNAR), imputation (k-NN, MICE)
Outlier detection (IQR, MAD, Cook's distance), robust statistics
Feature scaling and encoding (one-hot, target, hash), leakage-proof pipelines

Time series & forecasting

Decomposition, stationarity, lag/rolling/expanding features
Walk-forward and expanding-window cross-validation
SARIMA, ETS, Prophet; metrics like MASE, SMAPE, pinball loss
Gradient-boosted trees on lags, quantile regression, TFT/DeepAR concepts

Core modelling

Regularised linear and logistic regression (Ridge/Lasso/ElasticNet), odds ratios, VIF, learning curves
Decision trees and ensembles: bagging vs boosting, feature-importance caveats
Deep learning for tabular data: entity embeddings, MLPs, TabNet, TabTransformer

Evaluation & production concerns

Cost-sensitive thresholding, calibration (Platt/isotonic, Brier score)
Imbalanced-class tactics: class weights, focal loss, SMOTE
Distribution shift diagnostics (KS, PSI), drift monitoring, fairness metrics (demographic parity, equalised odds)

I’m modeling a VLSI circuit’s behaviour entirely through simulation and want to drive the process with an XGBoost Multi-Output Regressor. The goal is to replace portions of a traditional SPICE run with a machine-learning proxy that can predict multiple analog metrics at once (delay, power, noise, etc.) from layout-level or netlist-level features.

Key details
• Scope: pure simulation workflow; no verification or post-layout optimisation tasks are needed.
• Model choice: XGBoost Multi-Output Regressor is fixed.
• Critical tweak: I need a custom number of estimators rather than the default setting; help me decide and implement the optimal value through systematic tuning.

What I expect from you
1. Clean, reproducible Python (preferably a Jupyter Notebook) that:
– Pre-processes my CSV/netlist-derived dataset,
– Trains and cross-validates the XGBoost model,
– Outputs predictions for all target metrics in a form that can slot back into my current simulation flow.
2. Brief documentation describing feature engineering choices, chosen estimator count, and any other hyper-parameters.
3. A short note on how to extend the model to new process corners or larger datasets.

Tools on my side
I’m already set up with Python 3.10, scikit-learn, and xgboost on an Ubuntu workstation, so please align with that stack.

Deliver the notebook, a requirements.txt (if extra libraries are needed), and a sample run showing the model converging and producing multi-output predictions. If everything runs smoothly on my end and the metrics meet or beat my current SPICE run time/accuracy trade-off, the job is finished.

Skills: Python, Data Processing, Electronics, Machine Learning (ML), LabVIEW, Arduino, Data Science, Data Analysis

I’m putting together an end-to-end computer-vision pipeline that can automatically recognise and classify nature and landscape photographs. The core goal is a reliable image-classification model that can take a raw photo—forest, desert, coastline, mountain range, or any other natural scene we decide on—and return the correct label with strong accuracy.

Here’s what I need from you:
• A well-structured dataset or clear guidance on sourcing and curating one (public sets are fine as long as licensing is respected).
• A training workflow in Python using a mainstream framework such as TensorFlow or PyTorch, complete with data-augmentation, fine-tuning, and validation steps.
• Trained model weights plus inference code that runs on CPU or GPU with a single command.
• A concise README explaining environment setup, training parameters, and how to add new classes later.
• Evaluation metrics (precision, recall, confusion matrix) so I can judge performance quickly.

Acceptance will be based on reproducible training, cleanly commented code, and meeting an agreed-upon accuracy threshold on a held-out test set. If you’ve tackled similar nature-scene projects or have transfer-learning tricks up your sleeve, let’s get started—I’m ready to move fast once you are.

Skills: Java, Python, Software Architecture, Machine Learning (ML), Data Science, Computer Vision, Deep Learning, Data Augmentation

We need a mid-level experienced freelancer to analyze and normalize data from legacy utility billing and accounting software for import into new SQL databases. The ideal candidate will have experience in data analysis and software migration and have skills to use AI and know python scripting and work with data files and analyze them etc.

Be Part of Building Something New
This is an opportunity to be part of building something from the ground up — not simply developing another website or automation project, but helping create a new generation of AI agent-driven specialist media. Datacenter Focus is being designed as an intelligent editorial and commercial platform for the rapidly growing AI cloud and datacenter ecosystem, where AI agents will power research, content, audience development, data intelligence and commercial opportunity identification. You will have genuine ownership in shaping the technology, architecture and future direction of the platform, with the opportunity to grow alongside it as it scales from an ambitious new media venture into a global specialist intelligence and community business. For the right person, this is a chance to build the engine, shape the product and be part of the journey from day one.

Senior AI Automation & Data Platform Engineer — Datacenter Focus
About Datacenter Focus
Datacenter Focus is a new editorial and intelligence platform focused on the rapidly developing AI cloud and datacenter infrastructure ecosystem.
Our core editorial sectors are:
Power
Cooling
Networking
AI Data & Storage
Hardware
Security
The website is already built on WordPress using the Gutenberg/FSE block editor.
We are now building the AI-powered intelligence, editorial automation, audience growth and commercial engine behind the website.
This is an opportunity to build the core technology for a new media/intelligence business rather than simply maintain an existing website.
The role
We are looking for a senior AI Automation / AI Product Engineer who can design and build an integrated system connecting:
OpenAI / ChatGPT
Claude
Apollo
Clay
Make and/or n8n
Supabase/PostgreSQL
WordPress REST API
LinkedIn/social distribution
Newsletter platform
Analytics
CRM/commercial workflows
You will work directly with the founder and own the technical architecture and implementation.
This is a hands-on builder role.
We are not looking for someone who simply recommends tools or builds basic Zapier workflows.
We need someone who has actually built production-grade AI automation and data workflows.
________________________________________
What we want to build
1. AI Newsroom
Build an automated content intelligence pipeline that:
monitors relevant industry news and information sources
ingests new content
identifies relevant stories
removes duplicate stories
classifies stories by Datacenter Focus topic
identifies companies, people, technologies and locations
scores editorial relevance
researches additional context
generates article drafts
creates headlines and standfirsts
creates SEO metadata
creates related-company and related-story relationships
places content into a WordPress editorial queue
The system must have a human approval stage before publication.
________________________________________
2. Datacenter Focus company and people database
Integrate Apollo and Clay to build and continuously enrich a proprietary database of:
Companies
Hyperscalers
Neoclouds
GPU cloud providers
Colocation providers
Datacenter operators
OEMs
Infrastructure providers
Solution providers
Power companies
Cooling companies
Networking companies
Storage companies
Hardware companies
Security providers
Enterprise AI users
Consultancies
Technology providers
People
Relevant:
CEOs
CTOs
CIOs
CDOs
COOs
VP Infrastructure
VP Cloud
VP AI
Head of AI Infrastructure
Head of Datacenters
Head of Engineering
Marketing Directors
CMOs
Communications Directors
PR contacts
Product executives
Initial target:
1,000–2,000 companies
5,000–10,000 people
The database should include structured fields for company, person, sector, topic relevance, geography, seniority, role, contact information where legitimately available, Apollo ID, LinkedIn and engagement/commercial scores.
________________________________________
3. AI classification and intelligence
Every company, person and story should be capable of being associated with:
Power
Cooling
Networking
AI Data & Storage
Hardware
Security
AI Cloud
Datacenter operations
Geography
Technology
Company type
Create relevance scores such as:
AI infrastructure relevance
Datacenter relevance
Editorial relevance
Commercial relevance
Engagement score
________________________________________
4. Automated WordPress publishing
Integrate with the existing WordPress website using the WordPress REST API.
Approved AI-generated content should be capable of automatically creating/updating:
Posts
Categories
Tags
Featured images
SEO metadata where supported
Company profiles
Related content
Author/editorial information
The existing WordPress website must remain the front-end publishing platform.
Do not rebuild the website unless there is a compelling technical reason.
________________________________________
5. LinkedIn content automation
When an article is approved/published:
Extract the key points.
Generate a concise executive-level LinkedIn summary.
Generate an engaging opening.
Include the relevant Datacenter Focus topic.
Include the article URL.
Generate appropriate hashtags.
Queue/schedule the post.
The system should retain human approval initially.
________________________________________
6. Automated newsletter
Build an automated weekly newsletter workflow.
The AI should analyse the week's content and select:
Major stories
Important infrastructure developments
Companies to watch
Executive moves
Major datacenter projects
Emerging technologies
Important power/cooling/networking developments
It should then create the newsletter draft and send it to an approval queue.
________________________________________
7. SEO and organic audience-growth engine
Build an AI-driven content intelligence workflow that identifies:
emerging topics
high-value search opportunities
content gaps
underperforming pages
pages requiring updates
related content opportunities
internal-link opportunities
company/topic pages worth creating
evergreen intelligence pages
The objective is not mass AI content generation.
The objective is to build high-quality topical authority around AI cloud and datacenter infrastructure.
The system should be capable of maintaining evergreen intelligence pages such as:
AI Datacenter Cooling Companies
AI Datacenter Power Companies
GPU Cloud Providers
AI Networking Companies
AI Storage Companies
AI Datacenter Operators
European AI Datacenters
UK AI Datacenters
Middle East AI Datacenters
________________________________________
8. Audience intelligence
Integrate website and newsletter analytics into the database where technically and legally appropriate.
Build audience scoring based on signals such as:
article engagement
newsletter engagement
topic interest
returning visits
content consumption
LinkedIn engagement where available
event participation where available
The objective is to identify highly engaged executives and companies.
________________________________________
9. Commercial Intelligence Agent
Build an AI agent that continuously identifies potential commercial opportunities.
It should monitor:
company announcements
new datacenter projects
new products
funding
acquisitions
geographic expansion
AI infrastructure deployments
executive appointments
content engagement
newsletter engagement
website activity
It should produce a daily commercial opportunity list.
Example:
Company: XYZ
Signal: New 50MW AI facility
Topic: Power
Datacenter Focus engagement: High
Commercial score: 94/100
Potential opportunity:
Sponsored editorial
Research report sponsorship
Newsletter sponsorship
Executive interview
Event sponsorship
Recommended contacts:
CMO
VP Marketing
Communications Director
relevant product executive
________________________________________
10. Apollo commercial integration
The system should be capable of taking a qualified commercial opportunity and identifying relevant contacts through Apollo/Clay.
The AI should be capable of:
identifying the appropriate decision-maker
researching the company
identifying relevant recent activity
generating a personalised outreach recommendation
creating a draft email
Outbound campaigns should initially require human approval before Apollo sends them.
Apollo should manage approved sequences and follow-ups.
________________________________________
11. Partner reporting
Build an automated partner reporting system.
For each commercial partner, generate reports showing:
Audience
reach
engagement
geography
sector
seniority
company type
Content
article views
engagement
top-performing content
topic performance
LinkedIn performance
newsletter performance
Intelligence
companies engaging
executive engagement
geographic interest
emerging topics
recommended future content
The system should eventually be capable of producing a branded Datacenter Focus partner report automatically.
________________________________________
Technical requirements
The candidate should have strong practical experience with several of the following:
Required
OpenAI API
Claude API or Anthropic integrations
Make and/or n8n
REST APIs
Webhooks
Apollo API
Clay
PostgreSQL/Supabase
WordPress REST API
JSON
OAuth/API authentication
AI/LLM workflow design
Data modelling
Workflow monitoring
Error handling
Rate limiting
Deduplication
Strongly preferred
Python
JavaScript/TypeScript
GitHub
Docker
serverless/cloud deployment
CRM integration
email automation
analytics APIs
SEO automation
AI agents
structured LLM outputs
RAG/vector search
web scraping/data ingestion
________________________________________
Important characteristics
We are looking for someone who:
has built real production systems
understands APIs rather than just no-code interfaces
can work independently
can explain technical decisions clearly
understands data architecture
understands AI agents and workflow orchestration
understands commercial automation
can work directly with a non-technical founder
documents everything
builds systems that another developer can maintain
We are not looking for:
a junior developer learning AI
a chatbot developer
someone whose experience is primarily prompt engineering
a WordPress-only developer
a basic Zapier/Make freelancer
someone who wants to build everything manually
an agency that proposes a large team when the requirement is initially for one senior builder
________________________________________
Initial project
We would like to begin with a defined 8–12 week MVP build.
The initial objective is to demonstrate the complete workflow:
News discovered
→ AI classification
→ Company/person identification
→ Database
→ AI editorial draft
→ Human approval
→ WordPress publication
→ LinkedIn summary
→ Newsletter
→ Audience intelligence
→ Commercial opportunity
→ Apollo contact identification
The system should be designed so additional agents and commercial products can be added later.
________________________________________
Deliverables
At completion of the initial project:
Working AI newsroom
Working Apollo/Clay integration
Working company/person database
Working AI classification system
Working WordPress integration
Working editorial approval workflow
Working LinkedIn content workflow
Working newsletter workflow
Initial commercial intelligence agent
Initial partner reporting capability
Technical architecture documentation
Workflow documentation
Database schema documentation
API/integration documentation
Source code and workflow ownership transferred to Datacenter Focus
All credentials/accounts owned by Datacenter Focus
Handover session
Commercial terms
We are looking for an experienced senior individual on a project basis initially.
Please propose:
Fixed-price MVP
Estimated delivery time
Hourly/day rate for subsequent development
Availability
Relevant previous projects
Recommended technology architecture
Initial development budget:
£5,000–£10,000, depending on experience and proposed scope.
We are open to a longer-term relationship if the MVP is successful.
________________________________________
How to apply
Do not simply send a generic CV.
Please provide:
1. One relevant project
Show us an AI automation/data/agent system you have actually built.
2. Architecture
Explain in no more than 500 words how you would architect Datacenter Focus.
3. Demonstration
Provide a short Loom/video walkthrough of a live automation or AI agent you have built.
4. Technical assessment
Rate yourself from 1–5:
OpenAI API
Claude API
Make
n8n
Apollo
Clay
Supabase/PostgreSQL
WordPress API
Python
JavaScript/TypeScript
AI agents
APIs/webhooks
data enrichment
outbound automation
5. Commercial automation
Give one example of how you would use AI to identify a potential Datacenter Focus sponsor and determine who to contact.
6. Cost
Provide:
fixed MVP price
estimated timeline
hourly/day rate
ongoing availability
We are looking for a builder who can take ownership of the system, not somebody who simply follows a list of development tickets.

Fixed price AU$1,111 across four milestones, 3–4 weeks. Australian Registered Training Organisation (RTO 31871) needs a working pipeline that turns a messy SharePoint/OneDrive folder tree into a clean, indexed, AI-searchable knowledge base: deduplicate, convert Word/Excel/PowerPoint/PDF into Markdown twins with a standard YAML metadata header, generate a manifest index that resolves any question in one hop, apply a five-value classification scheme, and run an automated quality gate that blocks on a planted breach. Plus two scheduled agents and a runbook. Final milestone is a watched, unassisted pipeline run by our own manager. You build against a sanitised sample only — no live or student data. We own all output in portable formats. Needs 3 hours daily overlap with Australian Eastern time.

Role Overview
- As an AI Engineer, you will architect, implement, deploy, and scale advanced AI systems driven by Large Language Models (LLMs), multi-agent frameworks, retrieval-augmented generation (RAG) pipelines, and cloud ML infrastructure. Operating across the entire product lifecycle, you will partner directly with business units to convert complex operational hurdles into robust, secure, and production-grade applications.

Key Responsibilities
- Architect and code end-to-end AI applications utilizing LLMs, agentic orchestration, RAG architectures, and cloud services.
- Write production-grade, highly maintainable Python software for research, prototyping, and live deployments.
- Build, tune, and scale machine learning and predictive modeling pipelines.
- Implement rigorous MLOps practices to maximize system reliability, performance, and cost efficiency.
- Track and optimize continuous model metrics, operational output, and system utility.
- Translate ambiguous business requirements into practical, high-impact technical executions.
- Drive internal adoption and maturation of organizational AI capabilities.

Candidate Profile
- You are a proactive, curiosity-driven technologist eager to push the boundaries of modern AI. You thrive in collaborative environments, love breaking down complex architectural problems, and possess a passion for practical innovation.

Essential Qualifications & Experience
- Advanced Python engineering and software design fundamentals.
- Proven implementation history with LLMs, RAG patterns, and agentic frameworks (e.g., LangChain).
- Professional background spanning machine learning, data science, or MLOps.
- Working knowledge of major cloud ecosystems (AWS, Azure, or GCP).
- Exceptional communication skills capable of bridging technical and non-technical stakeholders.
- A proactive, continuous-learning mindset.

We need a data analyst to support our business by analyzing performance data and identifying trends that can improve operations. You will work with internal teams to gather requirements, process data, and present clear findings through reports and dashboards. The ideal freelancer can translate data into actionable recommendations and help us make better-informed decisions. This is a part-time role with room for growth based on performance.

I hold an MSc in Software Engineering and am applying for a fully funded PhD in Computer Science. I currently do not have a finalized research topic.

I want to hire an academic consultant (PhD holder or university researcher) to help me identify a viable topic, find an open research gap, and structure a high-impact research proposal.

To ensure my PhD is resilient against AI automation, my current preferences lean toward choosing a topic within one of the three high-demand Computer Science tracks listed below; however, I am fully open to new, innovative ideas if you have a stronger track in mind:
1. The Autonomous Agent Track: Multi-agent collaboration frameworks to automate complex enterprise software workflows.
2. The Neuro-Symbolic AI Track: Combining deep learning pattern recognition with logic-based computer science to build trustworthy, hallucination-free AI systems.
3. The Scalable Infrastructure Track: Optimizing algorithms to train and run massive models using drastically less compute power and energy.

IMPORTANT: Clarification on Scope (Phase 1 Focus)
Please note: We are NOT writing any proposal text during the initial milestones of this contract. The immediate goal of this project is strictly strategic. We are focusing 100% on identifying, validating, and locking down a viable, technically achievable research topic with a verified gap in current literature. Do not apply if you expect to immediately sit down and write a document. I am paying for your academic brainpower, market awareness, and technical vetting skills to ensure I do not get stuck with a bad topic later.

Milestone-Based Deliverables (To Avoid Getting Stuck):
This contract will be managed strictly via 4 distinct milestones. Payment will be released only upon the successful completion of each deliverable:

1. Milestone 1: Topic Selection and Refinement (
Deliverable: A 1-hour brainstorming session resulting in 3 concrete, specific research questions mapping my Software Engineering background to one of my preferred tracks or your newly suggested alternative.
2. Milestone 2: Literature Gap Validation
Deliverable: A curated list of at least 5-7 recent peer-reviewed papers (IEEE, ACM, NeurIPS) showing exactly where the current research ends and where my proposed topic begins.
3. Milestone 3: Proposal Outline and Methodology
Deliverable: A structured written outline detailing the proposed technical approach, algorithmic methods, and evaluation criteria for the chosen topic.
4. Milestone 4: Final Draft Review
Deliverable: Line-by-line academic review and polishing of my final 2-to-3 page proposal to ensure it meets rigorous CS department entry standards.

Candidate Requirements:
1. Must hold a PhD in Computer Science specializing in Artificial Intelligence, Machine Learning, or Distributed Systems.
2. Must have a proven track record of writing or reviewing successful doctoral research proposals.
3. Must act as a mentor/collaborator who guides the methodology; ghostwriters will be immediately reported.

Screening Questions for Applicants:
1. Based on my preferences (Agents, Neuro-Symbolic, Scalable Infrastructure) or your own vision, what specific Computer Science direction do you think holds the highest dynamic potential right now?
2. Briefly describe how you would help a student narrow down a broad idea into a specific, fundable CS research question during a 1-hour call without starting any structural draft writing.

NOTE: This is purely intellectual partnership. Money is not a motivation in this project.

Seeking an established researcher for long-term collaboration in computational biology and AI.

Requirements:

* PhD in CS/AI, computational science, bioinformatics, statistics, or related field
* Established publication record
* Professional standing to serve as a future academic/industry referee

Collaboration:

* Strengthen methodology and experimental design
* Perform supplementary analyses
* Co-write manuscripts and reviewer responses
* Provide a reference letter after an established collaboration

Full co-authorship for contributions meeting authorship standards, with opportunities for follow-up publications.

Skills: Statistics, Machine Learning (ML), Data Science, Computer Science, Deep Learning, Bioinformatics

Need a Data Scientist who can create relationships and logic for Automated revenue management (variable pricing), ECRIs, and Automated Marketing. This starts off with self-storage and later extends to Hotels, residential, and Logistics. Finding and pre-empting demand rather than Funnel Management

Seeking a PhD-level scientist with expertise in modeling complex, large-scale systems. Experience in climate-related or infrastructure modeling might be applicable.

The role involves developing and analyzing complex systems models to provide insights and solutions. Strong analytical and technical skills are essential.

This person needs a strong background in statistical modeling & the mathematics behind it.

I want a scientist who can help me navigate "cross-cause prioritization" modeling in philanthropic analysis.

We are looking for an experienced AI/ML Product Manager to lead the discovery, scoping, and deployment of our AI features. You will work directly with our engineering, data science, and design teams to take AI capabilities from concept to production.

Project Scope & Key Focus Areas

- LLM & RAG Feature Development: Scope and define acceptance criteria for user-facing generative AI tools, prompt workflows, and retrieval-augmented generation (RAG) capabilities.
- Model Evaluation & Quality Assurance: Establish model evaluation harnesses (monitoring precision, recall, latency, cost vs. accuracy tradeoffs, and hallucination rates) and hold engineering teams accountable to performance SLAs.
- Roadmap & Product Requirements: Translate business requirements into detailed PRDs, user stories, MVP definitions, and clear success KPIs.
- Buy / Build / Partner Strategy: Evaluate third-party foundation model APIs, open-source models, and specialized AI vendors to optimize architecture cost and performance.

Qualifications We Are Looking For

- 3+ years of core Product Management experience, with 1–2+ years specifically shipping AI/ML or LLM-powered SaaS products.
- Deep technical understanding of embeddings, vector databases, prompt engineering, fine-tuning, and MLOps lifecycles.
- Hands-on experience setting up evaluation sets (e.g., precision/recall, AUROC, LLM-as-a-judge frameworks).
- Strong executive-level presentation skills with the ability to explain complex AI trade-offs to non-technical stakeholders.

How to Apply (Submission Requirements)

To ensure your proposal is reviewed, please include the following in your response:
- Cover Letter & Case Example: A 2–3 sentence overview of an AI/LLM SaaS product or feature you recently managed from discovery to launch.
- Screening Questions:
-- What framework or metrics do you use to evaluate LLM output quality vs. latency and API costs?
-- Which foundation models or RAG tools have you evaluated or deployed in production?
- Introductory Loom Video (Required):
-- Please record a 1 to 2-minute Loom video introducing yourself.
-- Briefly share: (a) your background in AI product management and (b) one key challenge you faced when deploying an AI feature to production.
-- Important: Paste the shareable Loom link directly inside your Upwork proposal cover letter.

We are looking for an experienced AI / Data Automation Developer to help build an early-stage data intelligence MVP.

This is a commercial project involving the collection, enrichment, analysis and prioritisation of large datasets to produce actionable outputs for an internal business team.

We deliberately will not disclose the full commercial use case publicly. Further information will only be provided to shortlisted applicants.

What We Need

We need someone capable of taking information from multiple sources and building a system that can:

Collect and combine data from APIs and external datasets
Match and enrich records from different data sources
Work with location / geographic data
Store and structure large datasets
Develop a rules-based and/or AI scoring system
Rank opportunities based on multiple variables
Display prioritised results in a simple internal interface
Plot or visualise selected records geographically
Automate movement of selected records into other systems
Capture feedback from users to improve future scoring
Build the MVP so it can later scale significantly
Skills That Would Be Valuable

Experience with some of the following:

Python
PostgreSQL / PostGIS
GIS / geospatial data
APIs
Data engineering
AI / machine learning
Data enrichment
Scoring / ranking models
Automation
Google Maps or mapping APIs
CRM integrations
Airtable
Make
Zapier
Cloud databases
Dashboard or lightweight application development

You do not need to be an expert in every area.

We are more interested in finding someone who can understand a commercial problem, design the architecture and make the different components work together.

Initial Project

The first stage will be a small geographic pilot.

The objective is to prove that the system can:

Raw Data → Enrichment → Analysis → Scoring → Prioritisation → Map / Interface → Field Feedback

If the pilot works, there is potential for the project to expand into a much larger platform.

Who We Are Looking For

We want someone who:

Has built real-world data products before
Understands APIs and messy datasets
Can work independently
Thinks commercially, not just technically
Can recommend the simplest architecture rather than over-engineering
Is comfortable building an MVP quickly and improving it based on real-world testing

This is not simply a website development project.

It is primarily a data intelligence, automation and decision-support system.

When Applying

Please send:

Examples of similar systems you have built
Your experience with geospatial/location-based datasets
Your experience building scoring or ranking systems
Your preferred technology stack
A short explanation of how you would approach a project involving multiple datasets that need to be combined, analysed and prioritised

Please do not send a generic agency proposal.

We are specifically looking for someone who has personally worked on data engineering, AI, automation or intelligence platforms and can potentially become the technical lead for the project if the pilot is successful.

Potential for substantial ongoing work for the right person.

We’re looking for an experienced AI Automation Developer to help us build an MVP using Python, OpenAI APIs, and FastAPI.

The project involves designing and implementing practical AI-powered automation workflows, integrating LLM capabilities into backend services, and building a reliable foundation that can be expanded beyond the MVP.

What we’re looking for:

Strong experience with Python and FastAPI

Hands-on experience integrating OpenAI APIs and LLM-based applications

Experience building AI automation or workflow-based systems

Good understanding of REST APIs, backend architecture, and asynchronous processing

Ability to write clean, maintainable, and production-minded code

Strong problem-solving skills and ability to work independently


You’ll work closely with us to understand the requirements, design the approach, and implement the MVP from the ground up.

We’re looking for someone who can take ownership of the technical implementation, communicate clearly, and make sensible engineering decisions rather than simply following instructions.

Please include a brief overview of your relevant AI/automation projects, particularly anything involving OpenAI, FastAPI, or LLM-based applications.

We’re looking for an experienced artificial intelligence engineer to join the revolution, using deep learning, neuro-linguistic programming (NLP), computer vision, chatbots, and robotics to help us improve various business outcomes and drive innovation. The engineer will join a multidisciplinary team helping to shape our AI strategy and showcasing the potential for AI through early-stage solutions. This is an excellent opportunity to take advantage of emerging trends and technologies and make a real-world difference.

We're a growing architecture firm building out our data and reporting foundation, and we're looking for an experienced Data & BI Analyst to help own it.

You'll work directly with leadership, shape how we use data day to day, and build practical systems that improve operational and revenue decision-making. This is a hands-on role for someone comfortable moving between data modeling, dashboards, automation, and business analysis.

What you'll do
- Build and maintain dashboards in Power BI or Tableau covering sales, operations, financial performance, and company KPIs
- Write SQL to clean, model, and organize data for reliable reporting
- Use Python to automate recurring data workflows and eliminate manual work
- Develop practical forecasting and predictive models where they add value, including demand, revenue, or churn
- Identify inefficiencies and inconsistencies in our existing data processes and streamline them
- Help establish reliable reporting structures and definitions as our analytics capabilities grow
- Translate analysis into clear recommendations for non-technical stakeholders and company leadership

You're a fit if you have
- 7+ years of experience in data analytics, data science, business intelligence, or a closely related field
- Strong SQL and Python skills
- Hands-on dashboard development experience in Power BI or Tableau
- A track record of improving or automating data workflows with measurable efficiency, quality, or cost improvements
- The ability to independently own projects from initial problem definition through implementation
- Strong communication skills and the ability to work directly with non-technical leadership

Bonus
- Applied machine learning experience in a real business environment
- Graduate coursework or an advanced degree in analytics, data science, statistics, or a related discipline
- Experience with architecture, engineering, professional-services, or other project-based businesses

Scope & budget
- 10–20 hours/week to start
- Potential to grow into a long-term ongoing engagement
- $40–$80/hour depending on experience
- Fully remote
- Flexible schedule, with some overlap with U.S. business hours

To apply

Briefly describe a dashboard, analytical model, or data-workflow improvement you've delivered and the measurable impact it had.

If possible, include an anonymized example, screenshot, or description demonstrating the work.